The Fuzzy Economic Order Quantity Problem with a Finite Production Rate and Backorders

The track of developing Economic Order Quantity (EOQ) models with uncertainties described as fuzzy numbers has been very lucrative. In this paper, a fuzzy Economic Production Quantity (EPQ) model is developed to address a specific problem in a theoretical setting. Not only is the production time finite, but also backorders are allowed. The uncertainties, in the industrial context, come from the fact that the production availability is uncertain as well as the demand. These uncertainties will be handled with fuzzy numbers and the analytical solution to the optimization problem will be obtained. A theoretical example from the process industry is also given to illustrate the new model

A production inventory model with exponential demand rate and reverse logistics

The objective of this paper is to develop an integrated production inventory model for reworkable items with exponential demand rate. This is a three-layer supply chain model with perspectives of supplier, producer and retailer. Supplier delivers raw material to the producer and finished goods to the retailer. We consider perfect and imperfect quality products, product reliability and reworking of imperfect items. After screening, defective items reworked at a cost just after the regular manufacturing schedule. At the beginning, the manufacturing system starts produce perfect items, after some time the manufacturing system can undergo into “out-of-control” situation from “in-control” situation, which is controlled by reverse logistic technique. This paper deliberates the effects of business strategies like optimum order size of raw material, exponential demand rate, production rate is demand dependent, idle times and reverse logistics for an integrated marketing system. Mathematica is used to develop the optimal solution of production rate and raw material order for maximum expected average profit. A numerical example and sensitivity analysis is illustrated to validate the model

Sustainable Inventory Management Model for High-Volume Material with Limited Storage Space under Stochastic Demand and Supply

Inventory management and control has become an important management function, which is vital in ensuring the efficiency and profitability of a company’s operations. Hence, several research studies attempted to develop models to be used to minimise the quantities of excess inventory, in order to reduce their associated costs without compromising both operational efficiency and customers’ needs. The Economic Order Quantity (EOQ) model is one of the most used of these models; however, this model has a number of limiting assumptions, which led to the development of a number of extensions for this model to increase its applicability to the modern-day business environment. Therefore, in this research study, a sustainable inventory management model is developed based on the EOQ concept to optimise the ordering and storage of large-volume inventory, which deteriorates over time, with limited storage space, such as steel, under stochastic demand, supply and backorders. Two control systems were developed and tested in this research study in order to select the most robust system: an open-loop system, based on direct control through which five different time series for each stochastic variable were generated, before an attempt to optimise the average profit was conducted; and a closed-loop system, which uses a neural network, depicting the different business and economic conditions associated with the steel manufacturing industry, to generate the optimal control parameters for each week across the entire planning horizon. A sensitivity analysis proved that the closed-loop neural network control system was more accurate in depicting real-life business conditions, and more robust in optimising the inventory management process for a large-volume, deteriorating item. Moreover, due to its advantages over other techniques, a meta-heuristic Particle Swarm Optimisation (PSO) algorithm was used to solve this model. This model is implemented throughout the research in the case of a steel manufacturing factory under different operational and extreme economic scenarios. As a result of the case study, the developed model proved its robustness and accuracy in managing the inventory of such a unique industry

MULTI-OBJECTIVE ROBUST PRODUCTION PLANNING CONSIDERING WORKFORCE EFFICIENCY WITH A METAHEURISTIC SOLUTION APPROACH

Timely delivery of products to customers is one of the main factors of customer satisfaction and a key to the survival of a manufacturing system. Therefore, decreasing wasted time in manufacturing processes significantly affects production delivery time, which can be achieved through the maximization of workforce efficiency. This issue becomes more complicated when the parameters of the production system are under uncertainty. This paper presents a bi-objective scenario-based robust production planning model considering maximizing workforce efficiency and minimizing costs where the backorder, demand, and costs are uncertain. Also, backorder, raw materials purchasing, inventory control, and manufacturing time capacity are considered. A case study in a faucet manufacturing plant is considered to solve the model. Furthermore, the ε-constraint method, the Non-dominated Sorting Genetic Algorithm-II (NSGA-II), the Strength Pareto Evolutionary Algorithm 2 (SPEA2), and the Pareto Envelope-based Selection Algorithm II (PESA-II) are employed to solve the model. Also, the Taguchi method is used to tune the parameters of these algorithms. To compare these algorithms, five indicators are defined. The results show that the SPEA2 is the most time-consuming algorithm and the NSGA-II is the fastest, while their objective function values are nearly the same

An Inventory Model Considering All Unit Discount and Carbon Emissions

Consumer satisfaction is an important factor in the ongoing business process. Companies must be able to meet consumer demands and considers customers’ concerns on price. In a supplier and customer relationship, a given discount will affect the order size. Besides, in the current developing industry, environmental factors must be considered without disturbing the business. Recently, researchers and practitioners develop environmentally-friendly industries so that the environment will be well managed and not polluted. For example, carbon emissions can be managed by optimizing the production operation and product distribution. This paper presents a study on the relationship between discount on the economic order quantity model and the total carbon emissions. This research develops a procurement model by considering an all-unit discount system and carbon emission tax. The aim is to determine the optimal order that minimizes the total cost

Reconciling service levels by customers on a non-constrained production line for raw material and finished goods inventory levels

Supply chains are often under constant pressure to offer high service level to its customers by efficiently managing inventory levels despite the variability in demand, lead time etc. A national cat litter company, DEF Corporation, is trying to evaluate the optimal levels of inventory, both cycle stock and safety stock on a non-constrained production line which is constrained by the limited storage space for raw material. The research aims to develop an Economic Order Quantity (EOQ) model for DEF Corporation and to determine the amount of finished goods inventory essential to compensate for the limited amount of raw material inventory that could be stocked due to limited storage. The study would also calculate safety stocks to deal with demand uncertainties. The results would provide a model for calculating optimum levels of inventory that would manage demand variability. The study would be beneficial for DEF as it would avoid lost sales due to stock outs

A MODIFIED ECONOMIC PRODUCTION QUANTITY (EPQ) WITH SYNCHRONIZING DISCRETE AND CONTINUOUS DEMAND UNDER FINITE HORIZON PERIOD AND LIMITED CAPACITY OF STORAGE

The most popular inventory model to determine production lot size is the Economic Production Quantity (EPQ) model. It aids enterprises on how to minimize the total of production costs by reducing the inventory cost. However, the three main parameters in EPQ model, demand, set up cost, and holding cost, are not sufficient enough to solve current inventory issues. When an enterprise has two types of demand, continuous and discrete demands, the basic EPQ model would be no longer useful. Continuous demand comes from customers who want their demand to be fulfilled every time per unit time, while the fulfilment of discrete demand is at a fixed interval of time. Literature review is conducted to observe other formulations of EPQ model. As literature dealing with this problem cannot be found, this study aims to develop an EPQ model considering the two types of demand simultaneously. Therefore, this research proposes a modified EPQ model considering both continuous and discrete demands under finite horizon period. To find the solution of the model, three solution approaches were developed: (1) procedure approach, (2) algorithm approach, and (3) simultaneous approach. A numerical example is used to demonstrate the model. The solutions of the numerical example obtained using the three solution approaches are discusse